Whole-Genome Prediction of Type-2 Diabetes Susceptibility in Various Populations

描述（由申请人提供）：申请人的职业目标是成为统计遗传学领域的一名富有成效的独立研究者，特别是在基于基因组的2型糖尿病（T2 D）风险预测领域。为了实现这一目标，申请人提出了一个职业发展计划，其中包括应用于定量遗传学、分类和病例对照数据分析、应用于高维遗传数据的信息学以及T2 D的生物学和遗传学的统计学习方面的实践和教学培训。一个高度成就和多样化的调查人员与成功的辅导证明记录将监督申请人的职业发展。该项目的研究部分旨在提高我们使用遗传信息预测个人发展T2 D风险的能力。来自dbGaP（表型和基因型数据库）等来源的公开可用遗传和表型数据将用于开发和测试三个种族/种族群体中T2 D风险预测的各种模型。该项目将利用新开发的统计方法，这些方法能够同时整合数万个遗传标记的信息，这是对目前通常考虑不到100个标记的方法的重大进步。这项研究的目的是：1）在不同人群中检验T2 D的个体化全基因组预测（沿着标准协变量性别、年龄和BMI）将提供对当前基于遗传学的预测模型的重大改进，并将提供与基于家族史的预测相同或更高的准确性; 2）估算另外的遗传标记以确定是否可以改善预测，并鉴定在预测T2 D中最有用的标记子集; 3）通过将BMI和基因型的相互作用作为预测因子，建立给定一定体重指数（BMI）的T2 D风险的预测模型。该项目将大大提高我们预测不同人群中个体对T2 D易感性的能力，从而制定早期和有针对性的预防策略，将增加我们对T2 D遗传基础的理解，并将为Klimentidis博士作为独立科学家的发展提供关键培训。

DESCRIPTION (provided by applicant): The applicant's career goal is to become a productive independent investigator in the area of statistical genetics, particularly in the area of genomic-based prediction of type-2 diabetes (T2D) risk. To meet this goal, the applicant proposes a career development plan that includes hands- on and didactic training in statistical learning as applied to quantitative genetics, categorical and case-control data analysis, informatics as applied to high dimensional genetic data, and the biology and genetics of T2D. A highly accomplished and diverse set of investigators with proven track records of successful mentoring will oversee the applicant's career development. The research component of this project seeks to improve our ability to use genetic information to predict an individual's risk of developing T2D Publically available genetic and phenotypic data from sources such as dbGaP (The database of Phenotypes and Genotypes) will be used to develop and test various models for prediction of T2D risk among three racial/ethnic groups. This project will capitalize on newly developed statistical methods that are able to incorporate information from tens of thousands of genetic markers at once, which represent a major advance over current methods that typically take fewer than 100 markers into account. The aims of the study are: 1) To test the hypothesis, in different populations, that individualized whole-genome prediction of T2D (along with standard covariates of sex, age, and BMI) will offer major improvements over current genetics-based prediction models, and will offer equal or greater accuracy than prediction based on family history; 2) To impute additional genetic markers to determine whether prediction can be improved, and to identify the subset of markers that is most useful in predicting T2D; 3) To develop prediction models for T2D risk given a certain body mass index (BMI), by including as predictors the interaction of BMI and genotypes. This project will greatly enhance our ability to predict an individual's susceptibility to T2D within various populations, leading to earlier and targeted prevention strategies, will increase our understanding of the genetic basis of T2D, and will provide critical training for Dr. Klimentidis' development as an independent scientist.